搜索“DeepSeek”

shown in Figure 1. Figure 1: There’s a strong correlation between NLS depth and the number of layers in open-source models. Note: Models like Kimi-K3 that use Gated DeltaNets and DeepSeek-V4-Pro have significantly higher NLS depth per layer than the other more standard transformers listed in Figure 1. We think that the architectural changes that give Kimi-K3 higher depth is indeed somewhat concerning from a monitorability perspective. On the other hand, we consider DeepSeek-V4-Pro’s high depth

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ranks slightly lower overall might be excellent at the kind of coding work you do every day. For reference, the most popular open models in the space right now are GLM 5.3 Flash, DeepSeek V4 Flash , Kimi K3, and MiniMax M3 . These change very rapidly though. After you’ve selected a few models to try out, the next step is to find a provider that hosts them. The way these providers work is that you send them an API request and they run the model on their GPUs. You pay for the input you send to th

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27 2 分享 Avid Artifacts 的读者知道，我们不仅一直在报道模型，也报道它们的许可证，而且已经持续了相当长一段时间。曾经有一段时间，自定义许可证风靡一时，例如自定义的 Qwen2.5 72B-Instruct 许可证或 Llama 许可证。DeepSeek 曾为 DeepSeek V3 使用自定义许可证，后来 R1 将其改为 MIT，这导致许多（中国）模型厂商在 2025 年采用 MIT 或 Apache 2.0 许可证。 2026 年，开放模型比以往任何时候都更具竞争力，这带来了两个有趣的发展：西方模型厂商采用开放许可证，Google 和 Meta 都转向了 Apache 2.0。然而，处于前沿的中国模型厂商正变得越来越严格：Kimi K3 附带的许可证要求提供推理或微调服务者签订商业协议，MiniMax M3 则要求超过收入门槛的协议，并规定了禁止使用的场景。 最新的例子是智谱的 GLM-5.3，它从 MIT（GLM-5.2 及更早版本）改为自定义许可证，对推理和微调服务提供商有如下条款： 如果被许可方或其任何关联方经营模型即服务（Model as a Servic

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into one all-gather, removing an extra collective whose latency can dominate the transfer time for such small arrays. The PR reports roughly 80 microseconds per layer saved in its DeepSeek-V3 measurements. While 80 µs may sound small, DeepSeek-V3/R1 has 58 layers that require this AllGather. Across those layers, the savings add up to around 4.64 ms per forward pass in isolation (80 µs × 58), reducing TPOT and increasing interactivity. Google also implemented the ReduceScatter collective on Spars

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showing a surge in open models released by Chinese labs (below) like Alibaba with Qwen (which the report said had become the “community’s base model”), Moonshot AI with Kimi, and DeepSeek. Meanwhile, US companies AMD and Nvidia published the most new models. (below) I Another interesting trend: AI agents have become the number one user on Hugging Face Hub (below), which the company said could significantly change the numbers in the next report. In an interview with journalists, Delangue said he

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, et al. (2022). "Training language models to follow instructions with human feedback." Advances in Neural Information Processing Systems (NeurIPS). [12] Shao, Z., et al. (2024). "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models." arXiv preprint arXiv:2402.03300. [13] Song, H., et al. (2022). "Learning from noisy labels with deep neural networks: A survey." IEEE Transactions on Neural Networks and Learning Systems (TNNLS). [14] Han, B., et al. (2018). "Co-teachi

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nAI are discovering new vulnerabilities in industry-standard software, building POC exploits, and publishing the details in CVE descriptions; Open models such as Kimi K3, GLM-5.2, DeepSeek V4, Qwen 3.8, MiMo V2.5, MiniMax M3, Nemotron, Gemma, and Inkling are all rising in security benchmarks such as Cybench, NYU CTF Bench, AutoAdvExBench, and Cyberseceval 3. This makes it trivial for black hats to develop exploits from a CVE description as they can circumvent model guardrails Thus, when we began

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Qwen3.8-Flash on QwenCloud , priced at 0.15 USD per million input tokens and 0.47 USD per million output tokens . Performance Language Qwen3.8-Flash-Next Qwen3.8-27B Qwen3.7-Plus DeepSeek-V4-Flash-0731 Claude-Opus-4.6 (Max) Params 125B 27B 397B 284B -- Activated params 6B 27B 17B 13B -- N-gram embedding params 51B -- -- -- -- Coding Agentic coding DeepSWE 1.1 58.7 42.2 16.5 54.4 -- Agentic coding SWE-bench Pro 62.5 61.7 55.8 56.0 53.4 Multilingual software engineering SWE-bench Multilingual 81.

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undown: Nvidia joins SpaceX’s orbital AI build Thomson Reuters builds its own legal AI model Build a reusable AI design system with Open Design Chinese hackers double attacks with DeepSeek LATEST DEVELOPMENTS Image source: Nvidia The Rundown: SpaceX just announced it will build its space-based Starmind data centers around Nvidia’s Vera Rubin NVL72 rack, with Elon Musk revealing new details on a slimmed-down space version of the system he wants in orbit by Q4 next year. The details: Each rack pac

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esults for themselves. All of the data is open source and the community has the opportunity to draw their own conclusions on the current state of real world inference performance. DeepSeek V4 Pro 0813 DeepSeek V4 Pro 0813 is an ultra popular frontier open weight model from China. It has 1.6 Trillion parameters with 49 Billion active parameters. As of August 21, the following graph shows the best performance per SKU for all submissions, normalized by total cost of ownership (TCO). The ISL/OSL dis

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Comparing open vs. closed models across the eras of frontier models, Is the gap narrowing? The past two months have been a breakout period for open source AI. Yes, there was the “DeepSeek moment” back in January 2025, but no one actually used R1 to do any economically valuable work. In contrast, models like GLM 5.3 and Kimi K3 are genuinely capable of many of the same coding and agentic tasks that rocketed Anthropic to $65B+ ARR. Unlike others who inflated ARR, our figures were much closer to re

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he runtime determines what that agent is allowed to do. The harness layer is a spectrum rather than a fixed category. Codex and Claude Code are opinionated harnesses, while Pi and DeepSeek Harness (DSH) expose more of the harness as a programmable substrate. Through Cordis, DSH enables core behaviors that can be composed and replaced as plugins. This programmability makes the harness a poor place for a security guarantee: a layer designed to be modified cannot reliably enforce controls against i

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e cloud agents in Cursor. I took the top 1k research papers of the last year, summarized them, and visualized them. Fun fact: all 1,000 papers cost a total of $4 to summarize with DeepSeek V4 Flash. 4:44 PM · Aug 19, 2026 · 44.7K Views 37 Replies · 90 Reposts · 1.03K Likes Niels Rogge @NielsRogge My life has become boring All I do is asking coding agents to write a PLAN.md, then /grill-me on it to improve the plan, then spin up an agent to implement it I'm just a markdown manager now😭😭 2:18 PM ·

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why changing the effort invalidates the KV cache. I think closed GPT models call reasoning effort “juice,” since you can ask most models how much juice they have. In DwarfStar for DeepSeek with max reasoning this is added to the system prompt: Don’t Think The destination of reasoning tokens is therefore a learned convention: the model is trained to keep scratch work out of the final channel. Trick it into thinking it is in that channel and it may leak tokens. We have even seen older models, when

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g the generation pause within 3–5 seconds. Verified Day-0 Model Support Miles lands new frontier models on the day their weights become public, together with SGLang. Kimi-K3 [13], DeepSeek-V4 [10], Inkling [12], Qwen3.8 [14] and NVIDIA Nemotron 3 Ultra [11] were all trainable in Miles on release day, because the inference path in SGLang and the RL recipe in Miles are brought up in parallel rather than one after the other. Day-0 coverage is not limited to one architecture family either: it spans

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ing is getting more complex and more abstracted. The current open model ecosystem is buoyed by an explosion in interest in post-training open models. These people take models like DeepSeek V4 Flash, Inkling Small, or GLM 5.X and finetune them for their specific agentic tasks (e.g. in Tinker, the most popular finetuning API today). Over the last few years, post-training largely referred to the whole process of modifying the base model to make it intelligent and usable. There is a shift happening

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are the only accounts on the Hub where the heavy band carries the volume. Effectively all of MiniMax's 2026 downloads are of models above 70B, along with 88% of Moonshot's, 55% of DeepSeek's and 39% of Z.ai's. No large American account looks like this: Google, Microsoft and IBM Granite record essentially none of their 2026 downloads above 70B, and NVIDIA and Meta only 14% and 9%. The difference becomes clearer in total downloads. Moonshot’s frontier-only portfolio recorded 37M downloads over the

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Systematic Study of QKV Variants Explore and manage experiment logs in a web interface Explore code, traces, and workspace with an interactive logbook More Articles from our Blog DeepSeek-V4: a million-token context that agents can actually use 60 April 24, 2026 agents smolagents nlp Our Transformers Code Agent beats the GAIA benchmark 🏅 101 July 1, 2024 Community It was interesting to reproduce one of the ICML accepted papers using these tools. This analysis from the hackathon was particularly

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ent levels of intelligence.” Lightning also comes at a time when open models continue to roll into the market, particularly from vendors from China, such as Moonshot AI’s Kimi K3, DeepSeek’s various models, and Alibaba’s Qwen family. Stateside, Meta Glimmer this week rolled out Muse Glimmer, the first in a promised set of open models. (Meaning open weights, but not necessarily open source code for the training algorithm for the models.) Briski noted that over the past year, Nvidia has rolled out

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rence above. Where does the accuracy come from? The by-difficulty breakdown tells two different stories: Figure 1. Post-memory Task Goal Completion by difficulty, ours vs. ACE. On DeepSeek-V3.2 (right) we win Easy, Hard, and Overall; ACE only edges Medium. On gpt-oss-120b (left) ACE leads easy and medium, but per-task selection wins the hard tasks — and the aggregate. Each system improves from its own no-memory baseline (see the by-difficulty reference tables under Method notes below). The two m

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